Bayesian Variable Selection for Nowcasting Economic Time Series
We consider the problem of short-term time series forecasting (nowcasting) when there are more possible predictors than observations. Our approach combines three Bayesian techniques: Kalman filtering, spike-and-slab regression, and model averaging. We illustrate this approach using search engine query data as predictors for consumer sentiment and gun sales.
The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.
Bayesian Variable Selection for Nowcasting Economic Time Series, Steven L. Scott, Hal R. Varian. in Economic Analysis of the Digital Economy, Goldfarb, Greenstein, and Tucker. 2015